Spectrogram Window Comparison: Cough Sound Recognition using Convolutional Neural Network
Cough is one of the most common symptoms of diseases, especially respiratory diseases. Quick cough detection can be the key to the current pandemic of COVID-19. Good cough recognition is the one that uses non-intrusive tools such as a mobile phone microphone that does not disable human activities l...
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| Main Authors: | , , |
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| Format: | EJournal Article |
| Published: |
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.,
2022-07-31.
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| Subjects: | |
| Online Access: | Get Fulltext Get Fulltext |
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| Summary: | Cough is one of the most common symptoms of diseases, especially respiratory diseases. Quick cough detection can be the key to the current pandemic of COVID-19. Good cough recognition is the one that uses non-intrusive tools such as a mobile phone microphone that does not disable human activities like stick sensors. To do sound-only detection, Deep Learning current best method Convolutional Neural Network (CNN) is used. However, CNN needs image input while sound input differs (one dimension rather than two). An extra process is needed, converting sound data to image data using a spectrogram. When building a spectrogram, there is a question about the best size. This research will compare the spectrogram's size, called Spectrogram Window, by the performance. The result is that windows with 4 seconds have the highest F1-score performance at 92.9%. Therefore, a window of around 4 seconds will perform better for sound recognition problems. |
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| Item Description: | https://jurnal.ugm.ac.id/ijccs/article/view/75697 |